The priority heuristic is a simple, lexicographicdecision strategy that correctly predicts classic violations of expected utility theory such as the Allais paradox, the four-fold pattern, the certainty effect, the possibility effect, or intransitivities. The heuristic maps onto Rubinstein’s three-step-model, according to which people first check dominance and stop if it is present, otherwise they check for dissimilarity. To highlight Rubinstein’s model consider the following choice problem: I: 50% chance to win 2,000 II: 52% chance to win 1,000 Dominance is absent, and while chances are similar monetary outcomes are not. Rubinstein’s model predicts that people check for dissimilarity and consequently choose Gamble I. Unfortunately, dissimilarity checks are often not decisive, and Rubinstein suggested that people, proceed to a third step that he left unspecified. The priority heuristic elaborates on Rubinstein’s framework by specifying this Step 3.
Priority heuristic
For illustrative purposes consider a choice between two simple gambles of the type “a chance c of winning monetary amount x; a chance of winning amount y.” A choice between two such gambles contains four reasons for choosing: the maximum gain, the minimum gain, and their respective chances; because chances are complementary, three reasons remain: the minimum gain, the chance of the minimum gain, and the maximum gain. For choices between gambles in which all outcomes are positive or 0, the priority heuristic consists of the following three steps : Priority rule: Go through reasons in the order of minimum gain, chance of minimum gain, and maximum gain. Stopping rule: Stop examination if the minimum gains differ by 1/10 of the maximum gain; otherwise, stop examination if chances differ by 10%. Decision rule: Choose the gamble with the more attractive gain. The term “attractive” refers to the gamble with the higher gain and to the lower chance of the minimum gain.
Examples
Consider the following two choice problems, which were developed to support prospect theory, not the priority heuristic. Problem 1 A: 80% chance to win 4,000 B: 100% chance to win 3,000 Most people chose B. The priority heuristic starts by comparing the minimum gains of the Gambles A and B. The difference is 3,000, which is larger than 400, examination is stopped; and the heuristic predicts that people prefer the sure gain B, which is in fact the majority choice.A Problem 2 C: 45% chance to win 6,000 D: 90% chance to win 3,000 Most people chose Gamble D. The priority heuristic starts by comparing the minimum gains. Because they do not differ, the probabilities are compared. This difference is larger than 10%, examination stops and people are correctly predicted to choose D because of its higher probability of winning.
Empirical support and limitations
The priority heuristic correctly predicted the majority choice in all gambles in Kahneman and Tversky. Across four different data sets with a total of 260 problems, the heuristic predicted the majority choice better than cumulative prospect theory, two other modifications of expected utility theory, and ten well-known heuristics did. However, the priority heuristic fails to predict many simple decisions and has no free parameters, which triggered criticism, and countercriticism.
